SoroushVahidi/scidocs huggingface dataset:
A new active clamping zero-voltage switching PWM current-fed half-bridge converter
(2005)

A new active clamping zero-voltage switching (ZVS) pulse-width modulation (PWM) current-fed half-bridge converter (CFHB) is proposed in this paper. Its active clamping snubber (ACS) can not only absorb the voltage surge across the turned-off switch, but also achieve the ZVS of all power switches. Moreover, it can be applied to all current-fed power conversion topologies and its operation as well as structure is very simple. Since auxiliary switches in the snubber circuit are switched in a complementary way to main switches, an additional PWM IC is not necessary. In addition, it does not need any clamp winding and auxiliary circuit besides additional two power switches and one capacitor while the conventional current-fed half bridge converter has to be equipped with two clamp windings, two ZVS circuits, and two snubbers. Therefore, it can ensure the higher operating frequency, smaller-sized reactive components, lower cost of production, easier implementation, and higher efficiency. The operational principle, theoretical analysis, and design considerations are presented. To confirm the operation, validity, and features of the proposed circuit, experimental results from a 200-W, 24-200Vdc prototype are presented.
Landsat continuity : Issues and opportunities for land cover monitoring
(2008)

Initiated in 1972, the Landsat program has provided a continuous record of earth observation for 35 years. The assemblage of Landsat spatial, spectral, and temporal resolutions, over a reasonably sized image extent, results in imagery that can be processed to represent land cover over large areas with an amount of spatial detail that is absolutely unique and indispensable for monitoring, management, and scientific activities. Recent technical problems with the two existing Landsat satellites, and delays in the development and launch of a successor, increase the likelihood that a gap in Landsat continuity may occur. In this communication, we identify the key features of the Landsat program that have resulted in the extensive use of Landsat data for large area land covermapping andmonitoring.We then augment this list of key features by examining the data needs of existing large area land cover monitoring programs. Subsequently, we use this list as a basis for reviewing the current constellation of earth observation satellites to identify potential alternative data sources for large area land cover applications. Notions of a virtual constellation of satellites to meet large area land cover mapping and monitoring needs are also presented. Finally, research priorities that would facilitate the integration of these alternative data sources into existing large area land cover monitoring programs are identified. Continuity of the Landsat program and the measurements provided are critical for scientific, environmental, economic, and social purposes. It is difficult to overstate the importance of Landsat; there are no other systems in orbit, or planned for launch in the short-term, that can duplicate or approach replication, of the measurements and information conferred by Landsat. While technical and political options are being pursued, there is no satellite image data stream poised to enter the National Satellite Land Remote Sensing Data Archive should system failures occur to Landsat-5 and -7. Crown Copyright © 2007 Published by Elsevier Inc. All rights reserved.
Subcarrier-index modulation OFDM
(2009)

A new transmission approach, referred to as subcarrier-index modulation (SIM) is proposed to be integrated with the orthogonal frequency division multiplexing (OFDM) systems. More specifically, it relates to adding an additional dimension to the conventional two-dimensional (2-D) amplitude/phase modulation (APM) techniques, i.e. amplitude shift keying (ASK) and quadrature amplitude modulation (QAM). The key idea of SIM is to employ the subcarrier-index to convey information to the receiver. Furthermore, a closed-form analytical bit error ratio (BER) of SIM OFDM in Rayleigh channel is derived. Analytical and simulation results show error probability performance gain of 4 dB over 4-QAM OFDM systems for both coded and uncoded data without power saving policy. Alternatively, power saving policy retains an average gain of 1 dB while using 3 dB less transmit power per OFDM symbol.
Image Processing using IP Core Generator through FPGA
(2012)

Xilinx CORE Generator System generates and delivers parameterizable cores optimized for Xilinx FPGAs. CORE Generator is mainly used to create high density, high performance designs in Xilinx FPGAs in less time. The CORE Generator is included with the ISE WebPack and ISE Foundation software and comes with an extensive library of Xilinx LogiCORE IP. These include DSP functions, memories, storage elements, math functions and a variety of basic elements. Xilinx provides a flexible Block Memory Generator core to create compact, high-performance memories running at up to 450 MHz. Block Memory Generator provides single port and dual port block memory. These memory types differ in selection of operating modes.Matlab tool is used to convert the image that is being processed to .coe file format.
Improvement of the on-body performance of a dual-band textile antenna using an EBG structure
(2010)

A new dual-band wearable textile antenna designed for body-centric communications is introduced. The antenna is fully characterized in free space and on the body model, with and without an electromagnetic band gap (EBG) substrate. The band gap array consists of 3 × 3 elements and is used to reduce the interaction with human tissues. With the EBG back reflector, the radiation into the body is reduced by more than 15 dB. Increases of 5.2 dB and 3 dB gain are noticed at 2.45 GHz and 5.5 GHz, respectively. The results are presented for the return loss, radiation pattern, efficiency, and specific absorption rate (SAR).
Procedural City Layout Generation Based on Urban Land Use Models
(2009)

Training and simulation applications in virtual worlds require significant amounts of urban environments. Procedural generation is an efficient way to create such models. Existing approaches for procedural modelling of cities aim at facilitating the work of urban planners and artists, but either require expert knowledge or external input data to generate results that resemble real-life cities, or they have long computation times, and thus are unsuitable for non-experts such as training instructors. We propose a method that procedurally creates layouts of structurally plausible cities from high-level, intuitive user input such as city size, location and historic background. The resulting layouts consist of different kinds of city districts which are arranged using constraints derived from established models of urban land use. Our approach avoids the need for external expert engagement in the creation process, and allows for the generation of large city layouts in seconds, making it significantly faster than comparable agent-based software and thus supporting the needs of non-expert creators of virtual cities for many applications.
A modular crawler-driven robot: Mechanical design and preliminary experiments
(2009)

This paper presents a tracked robot composed of the proposed crawler mechanism, in which a planetary gear reducer is employed as the transmission device and provides two outputs in different forms with only one actuator. When the crawler moves in a rough environment, collision between mechanism and environment inevitably occurs. This under-actuated crawler can absorb the impact energy that should be transmitted to the actuator. A modular concept for the crawler is proposed for enlarging its use in robot systems and mechanical design of a modular crawler is conducted. Using this crawler module, a four-crawler-driven robot is realized by easily assembling. Experiments are conducted to verify the proposed concept and mechanical design. A single crawler module can well perform the proposed three locomotion modes. The four-crawler-driven robot has good adaptability to the environment which can get over obstacles both passively and actively.
An Algorithm for Training Polynomial Networks
(2013)

We consider deep neural networks, in which the output of each node is a quadratic function of its inputs. Similar to other deep architectures, these networks can compactly represent any function on a finite training set. The main goal of this paper is the derivation of an efficient layer-by-layer algorithm for training such networks, which we denote as the Basis Learner. The algorithm is a universal learner in the sense that the training error is guaranteed to decrease at every iteration, and can eventually reach zero under mild conditions. We present practical implementations of this algorithm, as well as preliminary experimental results. We also compare our deep architecture to other shallow architectures for learning polynomials, in particular kernel learning.
Deep Learning Approach for Secondary Structure Protein Prediction based on First Level Features Extraction using a Latent CNN Structure
(2017)

In Bioinformatics, Protein Secondary Structure Prediction (PSSP) has been considered as one of the main challenging tasks in this field. Today, secondary structure protein prediction approaches have been categorized into three groups (Neighbor-based, model-based, and meta predicator-based model). The main purpose of the model-based approaches is to detect the protein sequence-structure by utilizing machine learning techniques to train and learn a predictive model for that. In this model, different supervised learning approaches have been proposed such as neural networks, hidden Markov chain, and support vector machines have been proposed. In this paper, our proposed approach which is a Latent Deep Learning approach relies on detecting the first level features based on using Stacked Sparse Autoencoder. This approach allows us to detect new features out of the set of training data using the sparse autoencoder which will have used later as convolved filters in the Convolutional Neural Network (CNN) structure. The experimental results show that the highest accuracy of the prediction is 86.719% in the testing set of our approach when the backpropagation framework has been used to pre-trained techniques by relying on the unsupervised fashion where the whole network can be fine-tuned in a supervised learning fashion. Keywords—Secondary structure protein prediction; secondary structure; fine-tuning; Stacked Sparse; Deep Learning; CNN
A Method for Identification of Basmati Rice grain of India and Its Quality Using Pattern Classification
(2012)

The research work deals with an approach to perform texture and morphological based retrieval on a corpus of Basmati rice grain images. The work has been carried out using Image Warping and Image analysis approach. The method has been employed to normalize food grain images and hence eliminating the effects of orientation using image warping technique with proper scaling. The approach has been tested on sufficient number of basmati rice grain images of rice based on intensity, position and orientation. A digital image analysis algorithm based on color, morphological and textural features was developed to identify the six varieties of basmati rice seeds which are widely planted in India. Nine color and nine morphological and textural features were used for discriminant analysis. A back propagation neural network-based classifier was developed to identify the unknown grain types. The color and textural features were presented to the neural network for training purposes. The trained network was then used to identify the unknown grain types. It is also to find the percentage purity of hulled basmati rice grain sample by image processing technique. Commercially the purity test of basmati rice sample is done according to the size of the grain kernel (full, half or broken). By image processing we can also identify any broken basmati rice grains. Here we discuss the various procedures used to obtain the percentage quality of basmati rice grains. Keywords— warping, Image rectification, Image segmentation, Edge Detection, blurring image, Thresholding, Percentage Purity, Pixel area.
OCR binarization and image pre-processing for searching historical documents
(2007)

We consider the problem of document binarization as a pre-processing step for optical character recognition (OCR) for the purpose of keyword search of historical printed documents. A number of promising techniques from the literature for binarization, pre-filtering, and post-binarization denoising were implemented along with newly developed methods for binarization: an error diffusion binarization, a multiresolutional version of Otsu’s binarization, and denoising by despeckling. The OCR in the ABBYY FineReader 7.1 SDK is used as a black box metric to compare methods. Results for 12 pages from six newspapers of differing quality show that performance varies widely by image, but that the classic Otsu method and Otsu-based methods perform best on average. 2006 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Multi-phase Word Sense Embedding Learning Using a Corpus and a Lexical Ontology
(2016)

Word embeddings play a significant role in many modern NLP systems. However, most prevalent word embedding learning methods learn one representation per word which is problematic for polysemous words and homonymous words. To address this problem, we propose a multi-phase word sense embedding learning method which utilizes both a corpus and a lexical ontology to learn one embedding per word sense. We use word sense definitions and relations between word senses defined in a lexical ontology in a different way from existing systems. Experimental results on word similarity task show that our approach produces word sense embeddings of high quality.
A Study of Attribute-based Proxy Re-encryption Scheme in Cloud Environments
(2014)

Attribute-based proxy re-encryption (ABPRE) scheme is one of the proxy cryptography, which can delegate the reencryption capability to the proxy and re-encrypt the encrypted data by using the re-encryption key. ABPRE extending the traditional proxy cryptography and attributes plays an important role. In ABPRE, users are identified by attributes, and the access policy is designed to control the user’s access. Using ABPRE can have these advantages: (i) The proxy can be delegated to execute the re-encryption operation, which reduces the computation overhead of the data owner; (ii) The authorized user just uses his own secret key to decrypt the encrypted data, and he doesn’t need to store an additional decryption key for deciphering; (iii) The sensitive information cannot be revealed to the proxy in re-encryption, and the proxy only complies to the data owner’s command. In this paper, we survey two various access policy attribute-based proxy re-encryption schemes and analyze these schemes. Thereafter, we list the comparisons of them by some criteria.
Dual-Band Transmitarrays With Dual-Linear Polarization at Ka-Band
(2017)

Dual-band transmitarray antennas are demonstrated at Ka-band with the capability of forming independent linearly polarized beams with a given polarization in each frequency band, while sharing the same radiating aperture. The proposed three-layer unit-cell is based on identical narrow microstrip patches printed on both receiving and transmitting layers and connected by a metallized via hole. The metal layers are printed on two identical substrates bonded with a thin film, and the designed unit-cell exhibits a 180° phase resolution (i.e., 1-b phase quantization). The dual-band dual-polarized property of the transmitarray is achieved by interleaving unit-cells operating in the down-link and up-link frequency bands. Four different prototypes are characterized to demonstrate the relevance of the proposed concepts. A good agreement is obtained between the radiation patterns, gain curves, and cross-polarization levels measured and computed in both frequency bands and polarizations.
The diffusion of political memes in social media: keynote abstract
(2012)

This talk presents ongoing work on the study of information diffusion in social media, focusing in particular on political communication in the Twitter microblogging network. Social media platforms play an important role in shaping political discourse in the US and around the world. The truthy.indiana.edu infrastructure allows us to mine and visualize a large stream of social media data related to political themes. The analyses in this keynote address polarization and cross-ideological communication, and partisan asymmetries in the online political activities of social media users. Machine learning efforts can successfully leverage the structure of meme diffusion networks to detect orchestrated astroturf attacks that simulate grassroots campaigns, and to predict the political affiliation of active users. The retweet network segregates individuals into two distinct, homogenous communities of left- and right-leaning users. The mention network does not exhibit this kind of segregation, instead forming a communication bridge across which information flows between these two partisan communities. We propose a mechanism of action to explain these divergent topologies and provide statistical evidence in support of this hypothesis. Related to political communication are questions about the birth of online social movements. Social media data provides an opportunity to look for signatures that capture these seminal events. Finally, I will introduce a model of the competition for attention in social media. A dynamic of information diffusion emerges from this process, where a few ideas go viral while most do not. I will show that the relative popularity of different topics, the diversity of information to which we are exposed, and the fading of our collective interests for specific memes, can all be explained as deriving from a combination between the competition for limited attention and the structure of social networks. Surprisingly, one can reproduce the massive heterogeneity in the popularity and persistence of ideas without the need to assume different intrinsic values among those ideas.
Factorization machines with follow-the-regularized-leader for CTR prediction in display advertising
(2015)

Predicting ad click-through rates is the core problem in display advertising, which has received much attention from the machine learning community in recent years. In this paper, we present an online learning algorithm for click-though rate prediction, namely Follow-The-Regularized-Factorized-Leader (FTRFL), which incorporates the Follow-The-Regularized-Leader (FTRL-Proximal) algorithm with per-coordinate learning rates into Factorization machines. Experiments on a real-world advertising dataset show that the FTRFL method outperforms the baseline with stochastic gradient descent, and has a faster rate of convergence.
Culture and diverging views of social events.
(2005)

The authors compared East Asians' and Americans' views of everyday social events. Research suggests that Americans tend to focus more on the self and to have a greater sense of personal agency than East Asians. The authors assessed whether, compared to East Asians, Americans emphasize main characters even when events do not involve the self and whether they see more agency or intentionality in actions, even when the actions are not their own. Whether East Asians would observe more emotions in everyday scenarios than would Americans also was investigated. In Study 1, Chinese and Americans read alleged diary entries of another person. Americans did focus more on main characters and on characters' intentionality. Study 2 replicated these results comparing Taiwanese and Americans on free recall of events concerning the self and of narratives and videos concerning others. Study 2 also found that Taiwanese made more comments about the emotional states of characters.
Secure cloud computing: Benefits, risks and controls
(2011)

Cloud computing presents a new model for IT service delivery and it typically involves over-a-network, on-demand, self-service access, which is dynamically scalable and elastic, utilising pools of often virtualized resources. Through these features, cloud computing has the potential to improve the way businesses and IT operate by offering fast start-up, flexibility, scalability and cost efficiency. Even though cloud computing provides compelling benefits and cost-effective options for IT hosting and expansion, new risks and opportunities for security exploits are introduced. Standards, policies and controls are therefore of the essence to assist management in protecting and safeguarding systems and data. Management should understand and analyse cloud computing risks in order to protect systems and data from security exploits. The focus of this paper is on mitigation for cloud computing security risks as a fundamental step towards ensuring secure cloud computing environments.
Improving the Neural Algorithm of Artistic Style
(2016)

In this work we investigate different avenues of improving the Neural Algorithm of Artistic Style [7]. While showing great results when transferring homogeneous and repetitive patterns, the original style representation often fails to capture more complex properties, like having separate styles of foreground and background. This leads to visual artifacts and undesirable textures appearing in unexpected regions when performing style transfer. We tackle this issue with a variety of approaches, mostly by modifying the style representation in order for it to capture more information and impose a tighter constraint on the style transfer result. In our experiments, we subjectively evaluate our best method as producing from barely noticeable to significant improvements in the quality of style transfer.
Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream.
(2015)

Converging evidence suggests that the primate ventral visual pathway encodes increasingly complex stimulus features in downstream areas. We quantitatively show that there indeed exists an explicit gradient for feature complexity in the ventral pathway of the human brain. This was achieved by mapping thousands of stimulus features of increasing complexity across the cortical sheet using a deep neural network. Our approach also revealed a fine-grained functional specialization of downstream areas of the ventral stream. Furthermore, it allowed decoding of representations from human brain activity at an unsurpassed degree of accuracy, confirming the quality of the developed approach. Stimulus features that successfully explained neural responses indicate that population receptive fields were explicitly tuned for object categorization. This provides strong support for the hypothesis that object categorization is a guiding principle in the functional organization of the primate ventral stream.